Achieving Consensus in Robot Swarms

This book focuses on the design and analysis of collective decision-making strategies for the best-of-n problem. After providing a formalization of the structure of the best-of-n problem supported by a comprehensive survey of the swarm robotics literature, it introduces the functioning of a collective decision-making strategy and identies a set of mechanisms that are essential for a strategy to solve the best-of-n problem. The best-of-n problem is an abstraction that captures the frequent requirement of a robot swarm to choose one option from of a nite set when optimizing benets and costs. The book leverages the identication of these mechanisms to develop a modular and model-driven methodology to design collective decision-making strategies and to analyze their performance at different level of abstractions. Lastly, the author provides a series of case studies in which the proposed methodology is used to design different strategies, usingrobot experiments to show how the designed strategies can be ported to different application scenarios.

Covers collective decision-making strategies for robot swarms Focuses on the design of self-organized solutions to the best-of-n problemthe problem of deciding which alternative among a finite set of options is the most beneficial choice for the swarm Deals with both theoretical and experimental aspects of collective decision-making and includes the results of experiments performed with a swarm of 100 robots Includes supplementary material: sn.pub/extras

Inhalt
Introduction.- Part 1:Background and Methodology.- Discrete Consensus Achievement in Articial Systems.- Modular Design of Strategies for the Best-of-n Problem.- Part 2:Mathematical Modeling and Analysis.- Indirect Modulation of Majority-Based Decisions.- Direct Modulation of Voter-Based Decisions.- Direct Modulation of Majority-Based Decisions.- Part 3:Robot Experiments.- A Robot Experiment in Site Selection.- A Robot Experiment in Collective Perception.- Part 4:Discussion and Annexes.- Conclusions.- Background on Markov Chains.